Australasian Journal of Information Systems

Editor’s Comment

Karlheinz Kautz — 2024-05-16

This is the third and my farewell editorial as editor-in-chief (EiC) of the AJIS. Three plus years, 40 months have passed since I took over the EiC role after an appointment by the AAIS search panel.

Machine Learning Based Decision-Making: A Sensemaking Perspective

Jingqi (Celeste) Li — 2024-05-15

The integration of machine learning (ML), functioning as the core of various artificial intelligence (AI)-enabled systems in organizations, comes with the assertion that ML models offer automated decisions or assist domain experts in refining their decision-making. The current research presents substantial evidence of ML’s positive impact on business and organizational performance. Nonetheless, there is a limited understanding of how decision-makers participate in the process of generating ML-driven insights and enhancing their comprehension of business environments through ML outcomes. To enhance this engagement and understanding, this study examines the interactive process between decision-makers and ML experts as they strive to comprehend an environment and gather business insights for decision-making. It builds upon Weick’s sensemaking model by integrating ML’s pivotal role. By conducting interviews with 31 ML experts and ML end-users, we explore the dimensions of sensemaking in the context of ML utilization for decision-making. Consequently, this study proposes a process model which advances the organizational ML research by operationalizing Weick’s work into a structured ML-driven sensemaking model. This model charts a pragmatic pathway, outlining the interaction sequence between decision-makers and ML tools as they navigate through recognizing and utilizing ML, exploring opportunities, assessing ML model outcomes, and translating ML models into action, thereby advancing both the theoretical framework and its practical deployment in organizational contexts.

Sociotechnical perspectives of digital technologies in sustainable mining

Warren Gabryk — 2024-05-15

This paper adopts an interpretive case study approach to understand the role of digital technologies in addressing seemingly contradictory sustainability goals in mining. The sociotechnical model of information systems was used as a framework to guide the analysis of twenty-five in-depth interviews with globally dispersed digital technology experts working collaboratively at an industry-leading hi-tech mining solutions company. The sociotechnical-led thematic analysis findings highlight the trade-offs experts face in balancing narrow technological imperatives and economic outcomes with broader sustainability goals. The analysis moves beyond the technological and economic to a harmonious perspective of social, human, environmental, and technological interactions. A visual thematic map is presented to aid practitioners in designing and optimally implementing digital technologies to simultaneously address the United Nations Sustainable Development Goals while prioritising business sustainability. We conclude by drawing from the proposed sociotechnical perspectives approach for digital sustainability to provide scholars with possible pathways for future responsible information systems research.

“Use” as a Conscious Thought: Towards a Theory of “Use” in Autonomous Things

Gohar Khan — 2024-05-15

The way users perceive and use information systems artefacts has been mainly studied from the notion of behavioral beliefs, deliberate cognitive efforts, and physical actions performed by human actors to produce certain outcomes. The next generation of information systems, however, can sense, respond, and adapt to environments without necessitating similar cognitive efforts, physical contact, or explicit instructions to operate. Therefore, by leveraging theories of consciousness and technology use, this research aims to advance an alternative understanding of the "use" associated with the next generation of IS artefacts that do not require deliberate cognitive efforts, physical manipulation, or explicit instructions to yield outcomes. The theory and proposed model were refined and validated through the burst detection technique, IS expert involvement (n=10), a pilot study (n=130), and end-user surveys (n= 119). Structural equating modelling techniques were employed to test the theory. We show that unlike the manually operated IS artefacts, the “use” of a fully autonomous artefact is a conscious thought rather than a physical activity of operating a system to produce certain outcomes. We argue that, unlike the traditional notions of use associated with manually operated technologies, conscious use is not characterized solely by behavioral beliefs stemming from logical and reflective cognitive and physical efforts (e.g., effort expectancy). We propose the notion of conscious use within the context of fully autonomous entities and empirically validate its measure. Additionally, we offer recommendations for future research directions in this area. The conceptualization of this new theory for fully autonomous IS artefacts adds significant academic value to the literature given the convergence of AI-based machine learning systems and cognitive computing systems.

(Why) Do We Trust AI?: A Case of AI-based Health Chatbots

Ashish Viswanath Prakash — 2024-05-15

Automated chatbots powered by artificial intelligence (AI) can act as a ubiquitous point of contact, improving access to healthcare and empowering users to make effective decisions. However, despite the potential benefits, emerging literature suggests that apprehensions linked to the distinctive features of AI technology and the specific context of use (healthcare) could undermine consumer trust and hinder widespread adoption. Although the role of trust is considered pivotal to the acceptance of healthcare technologies, a dearth of research exists that focuses on the contextual factors that drive trust in such AI-based Chatbots for Self-Diagnosis (AICSD). Accordingly, a contextual model based on the trust-in-technology framework was developed to understand the determinants of consumers’ trust in AICSD and its behavioral consequences. It was validated using a free simulation experiment study in India (N = 202). Perceived anthropomorphism, perceived information quality, perceived explainability, disposition to trust technology, and perceived service quality influence consumers’ trust in AICSD. In turn, trust, privacy risk, health risk, and gender determine the intention to use. The research contributes by developing and validating a context-specific model for explaining trust in AICSD that could aid developers and marketers in enhancing consumers’ trust in and adoption of AICSD.

What prevents organisations from achieving e-HRM potential?

Arnela Ceric — 2024-01-29

Use of electronic human resource management (e-HRM) offers the prospect of enabling the human resource management (HRM) function to take on a strategic partner’s role in organisations. Despite the pervasive expansion of e-HRM use, there is no clear understanding of why organisations are not achieving e-HRM potential. We address this issue by investigating e-HRM adoption factors and their influence on information technology (IT) use potential to automate, informate and transform the HRM function in a sequential manner. In particular, we examine HRM professionals’ experiences with e-HRM use, including challenges, successes, and outcomes. We identified e-HRM adoption factors that enable and that constrain each stage of e-HRM use. With a focus on the inhibiting factors, our findings suggest that e-HRM potential hindered already in the automation stage diminishes e-HRM potential to subsequently informate and to transform the e-HRM function.

Using Analytical Information for Digital Business Transformation through DataOps: A Review and Conceptual Framework

Jia Xu — 2024-01-29

Organisations are increasingly practising business analytics to generate actionable insights that can guide their digital business transformation. Transforming business digitally using business analytics is an ongoing process that requires an integrated and disciplined approach to leveraging analytics and promoting collaboration. An emerging business analytics practice, Data Operations (DataOps), provides a disciplined approach for organisations to collaborate using analytical information for digital business transformation. We propose a conceptual framework by reviewing the literature on business analytics, DataOps and organisational information processing theory (OIPT). This conceptual framework explains how organisations can employ DataOps as an integrated and disciplined approach for developing the analytical information processing capability and facilitating boundary-spanning activities required for digital business transformation. This research (a) extends current knowledge on digital transformation by linking it with business analytics from the perspective of OIPT and boundary-spanning activities, and (b) presents DataOps as a novel approach for using analytical information for digital business transformation.

Mobile Identity Protection: The Moderation Role of Self-Efficacy

Yasser Alhelaly — 2024-01-29

The rapid growth of mobile applications and the associated increased dependency on digital identity raises the growing risk of identity theft and related fraud. Hence, protecting identity in a mobile environment is a problem. This study develops a model that examines the role of identity protection self-efficacy in increasing users’ motivation intentions to achieve actual mobile identity protection. Our research found that self-efficacy significantly affects the relationship between users’ perceived threat appraisal and their motivational intentions for identity protection. The relation between mobile users’ protection, motivational intentions, and actual mobile identity protection actions was also found to be significant. Additionally, the findings revealed the considerable impact of awareness in fully mediating between self-efficacy and actual identity protection. The model and its hypotheses are empirically tested through a survey of 383 mobile users, and the findings are validated through a panel of experts, thus confirming the impact of self-efficacy on an individual’s identity protection in the mobile context.

Doing Big Things in a Small Way: A Social Media Analytics Approach to Information Diffusion During Crisis Events in Digital Influencer Networks

Shohil Kishore — 2024-01-28

Digital influencers play an essential role in determining information diffusion during crisis events. This paper demonstrates that information diffusion (retweets) on the social media platform Twitter (now X) highly depends on digital influencers’ number of followers and influencers’ location within communication networks. We show (study 1) that there is significantly more information diffusion in regional (vs. national or international) crisis events when tweeted by micro-influencers (vs. meso- and macro-influencers). Further, study 2 demonstrates that this pattern holds when micro-influencers operate in a local location (are located local to the crisis). However, effects become attenuated when micro-influencers are situated in a global location (outside of the locality of the event). We term this effect ‘influencer network compression’ – the smaller in scope a crisis event geography (regional, national, or international) and influencer location (local or global) becomes, the more effective micro-influencers are at diffusing information. This shows that those who possess the most followers (meso- and macro-influencers) are less effective at attracting retweets than micro-influencers situated local to a crisis. As online information diffusion plays a critical role during public crisis events, this paper contributes to both practice and theory by exploring the role of digital influencers and their network geographies in different types of crisis events.